Table 6.
A combination of detection and segmentation-based methods for forest fire monitoring and surveillance.
| Authors | Year | Method | Architecture | Accuracy | Type | Augmentation | Type of Data |
|---|---|---|---|---|---|---|---|
| Bai and Wang [74] | 2021 | CNN | YOLO & VGG network | Accuracy - 96.5% | Detection & Classification | Yes | Image |
| Fan and Pei [75] | 2021 | CNN | YOLOv4 & MobileNet | mAP - 75.72 | Detection & Classification | No | Image |
| Wei et al. [87] | 2022 | CNN | YOLOv5S & Mobilenetv3 | Accuracy - 90.5% | Detection & Classification | Yes | Image |
| Tran et al. [89] | 2022 | DetNAS | ShuffleNetV2, Faster R-CNN model with VoVNet, ResNet, & FBNetV3 | mAP - 27.9 | Detection & Classification | Yes | Image |